
'''simple docstring'''





import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


SCREAMING_SNAKE_CASE     =       datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
      import pyspark






@dataclass
class 							UpperCAmelCase_							(    datasets.BuilderConfig				):

 '''simple docstring'''
 lowercase_					:    Optional[Any]				=							None







def snake_case_     (					lowercase__   ,       lowercase__   ,       ):
  import pyspark

  def generate_fn():
    UpperCAmelCase__      :					Union[str, Any]				       =							df.select("*"   ,       pyspark.sql.functions.spark_partition_id().alias("part_id"		)		)
    for partition_id in partition_order:
      UpperCAmelCase__      :					str				       =							df_with_partition_id.select("*"		).where(F"""part_id = {partition_id}"""		).drop("part_id"		)
      UpperCAmelCase__      :					Optional[Any]				       =							partition_df.collect()
      UpperCAmelCase__      :					List[Any]				       =							0
      for row in rows:
        yield F"""{partition_id}_{row_id}""", row.asDict()
        row_id += 1

  return generate_fn






class 							UpperCAmelCase_							(    _BaseExamplesIterable				):

 '''simple docstring'''







 def __init__(					self		:  List[str]			,				snake_case__		:  "pyspark.sql.DataFrame"			,				snake_case__		:  Dict=None			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							df
   UpperCAmelCase__      :					Tuple				       =							partition_order or range(self.df.rdd.getNumPartitions() )
   UpperCAmelCase__      :					List[Any]				       =							_generate_iterable_examples(self.df			,				self.partition_order )







 def __iter__(					self		:  Tuple ):



   '''simple docstring'''




   yield from self.generate_examples_fn()







 def 							UpperCamelCase  (					self		:  Optional[int]			,				snake_case__		:  np.random.Generator ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							list(range(self.df.rdd.getNumPartitions() ) )
   generator.shuffle(snake_case__ )
   return SparkExamplesIterable(self.df			,				partition_order=snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  int			,				snake_case__		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							self.split_shard_indices_by_worker(snake_case__			,				snake_case__ )
   return SparkExamplesIterable(self.df			,				partition_order=snake_case__ )







 @property
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   return len(self.partition_order )






class 							UpperCAmelCase_							(    datasets.DatasetBuilder				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							SparkConfig







 def __init__(					self		:  int			,				snake_case__		:  "pyspark.sql.DataFrame"			,				snake_case__		:  str = None			,				snake_case__		:  str = None			,				**snake_case__		:  Optional[Any]			,				):



   '''simple docstring'''




   import pyspark

   UpperCAmelCase__      :					str				       =							pyspark.sql.SparkSession.builder.getOrCreate()
   UpperCAmelCase__      :					Optional[Any]				       =							df
   UpperCAmelCase__      :					Optional[int]				       =							working_dir

   super().__init__(
       cache_dir=snake_case__			,				config_name=str(self.df.semanticHash() )			,				**snake_case__			,				)







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   def create_cache_and_write_probe(snake_case__		:  Optional[int] ):
     # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
     # already exist.
     os.makedirs(self._cache_dir			,				exist_ok=snake_case__ )
     UpperCAmelCase__      :					str				       =							os.path.join(self._cache_dir			,				"fs_test" + uuid.uuida().hex )
     # Opening the file in append mode will create a new file unless it already exists, in which case it will not
     # change the file contents.
     open(snake_case__			,				"a" )
     return [probe_file]

   if self._spark.conf.get("spark.master"			,				"" ).startswith("local" ):
     return

   # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
   # accessible to the driver.
   # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
   if self._cache_dir:
     UpperCAmelCase__      :					Any				       =							(
         self._spark.sparkContext.parallelize(range(1 )			,				1 ).mapPartitions(snake_case__ ).collect()
     )
     if os.path.isfile(probe[0] ):
       return

   raise ValueError(
       "When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir" )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   return datasets.DatasetInfo(features=self.config.features )







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  datasets.download.download_manager.DownloadManager ):



   '''simple docstring'''




   return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[Any] ):



   '''simple docstring'''




   import pyspark

   def get_arrow_batch_size(snake_case__		:  Any ):
     for batch in it:
       yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]} )

   UpperCAmelCase__      :					str				       =							self.df.count()
   UpperCAmelCase__      :					Tuple				       =							df_num_rows if df_num_rows <= 1_00 else 1_00
   # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
   UpperCAmelCase__      :					Optional[Any]				       =							(
       self.df.limit(snake_case__ )
       .repartition(1 )
       .mapInArrow(snake_case__			,				"batch_bytes: long" )
       .agg(pyspark.sql.functions.sum("batch_bytes" ).alias("sample_bytes" ) )
       .collect()[0]
       .sample_bytes
       / sample_num_rows
   )
   UpperCAmelCase__      :					Optional[int]				       =							approx_bytes_per_row * df_num_rows
   if approx_total_size > max_shard_size:
     # Make sure there is at least one row per partition.
     UpperCAmelCase__      :					Optional[int]				       =							min(snake_case__			,				int(approx_total_size / max_shard_size ) )
     UpperCAmelCase__      :					Optional[int]				       =							self.df.repartition(snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  str			,				snake_case__		:  str			,				snake_case__		:  int			,				):



   '''simple docstring'''




   import pyspark

   UpperCAmelCase__      :					Dict				       =							ParquetWriter if file_format == "parquet" else ArrowWriter
   UpperCAmelCase__      :					Tuple				       =							os.path.join(self._working_dir			,				os.path.basename(snake_case__ ) ) if self._working_dir else fpath
   UpperCAmelCase__      :					int				       =							file_format == "parquet"

   # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
   # pickling the SparkContext.
   UpperCAmelCase__      :					List[str]				       =							self.config.features
   UpperCAmelCase__      :					Union[str, Any]				       =							self._writer_batch_size
   UpperCAmelCase__      :					Tuple				       =							self._fs.storage_options

   def write_arrow(snake_case__		:  Any ):
     # Within the same SparkContext, no two task attempts will share the same attempt ID.
     UpperCAmelCase__      :					int				       =							pyspark.TaskContext().taskAttemptId()
     UpperCAmelCase__      :					Dict				       =							next(snake_case__			,				snake_case__ )
     if first_batch is None:
       # Some partitions might not receive any data.
       return pa.RecordBatch.from_arrays(
           [[task_id], [0], [0]]			,				names=["task_id", "num_examples", "num_bytes"]			,				)
     UpperCAmelCase__      :					Any				       =							0
     UpperCAmelCase__      :					Optional[Any]				       =							writer_class(
         features=snake_case__			,				path=working_fpath.replace("SSSSS"			,				F"""{shard_id:05d}""" ).replace("TTTTT"			,				F"""{task_id:05d}""" )			,				writer_batch_size=snake_case__			,				storage_options=snake_case__			,				embed_local_files=snake_case__			,				)
     UpperCAmelCase__      :					List[Any]				       =							pa.Table.from_batches([first_batch] )
     writer.write_table(snake_case__ )
     for batch in it:
       if max_shard_size is not None and writer._num_bytes >= max_shard_size:
         UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							writer.finalize()
         writer.close()
         yield pa.RecordBatch.from_arrays(
             [[task_id], [num_examples], [num_bytes]]			,				names=["task_id", "num_examples", "num_bytes"]			,				)
         shard_id += 1
         UpperCAmelCase__      :					str				       =							writer_class(
             features=writer._features			,				path=working_fpath.replace("SSSSS"			,				F"""{shard_id:05d}""" ).replace("TTTTT"			,				F"""{task_id:05d}""" )			,				writer_batch_size=snake_case__			,				storage_options=snake_case__			,				embed_local_files=snake_case__			,				)
       UpperCAmelCase__      :					Optional[int]				       =							pa.Table.from_batches([batch] )
       writer.write_table(snake_case__ )

     if writer._num_bytes > 0:
       UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							writer.finalize()
       writer.close()
       yield pa.RecordBatch.from_arrays(
           [[task_id], [num_examples], [num_bytes]]			,				names=["task_id", "num_examples", "num_bytes"]			,				)

     if working_fpath != fpath:
       for file in os.listdir(os.path.dirname(snake_case__ ) ):
         UpperCAmelCase__      :					Dict				       =							os.path.join(os.path.dirname(snake_case__ )			,				os.path.basename(snake_case__ ) )
         shutil.move(snake_case__			,				snake_case__ )

   UpperCAmelCase__      :					Tuple				       =							(
       self.df.mapInArrow(snake_case__			,				"task_id: long, num_examples: long, num_bytes: long" )
       .groupBy("task_id" )
       .agg(
           pyspark.sql.functions.sum("num_examples" ).alias("total_num_examples" )			,				pyspark.sql.functions.sum("num_bytes" ).alias("total_num_bytes" )			,				pyspark.sql.functions.count("num_bytes" ).alias("num_shards" )			,				pyspark.sql.functions.collect_list("num_examples" ).alias("shard_lengths" )			,				)
       .collect()
   )
   for row in stats:
     yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)







 def 							UpperCamelCase  (					self		:  List[Any]			,				snake_case__		:  "datasets.SplitGenerator"			,				snake_case__		:  str = "arrow"			,				snake_case__		:  Optional[Union[str, int]] = None			,				snake_case__		:  Optional[int] = None			,				**snake_case__		:  Union[str, Any]			,				):



   '''simple docstring'''




   self._validate_cache_dir()

   UpperCAmelCase__      :					Optional[Any]				       =							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
   self._repartition_df_if_needed(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							not is_remote_filesystem(self._fs )
   UpperCAmelCase__      :					Optional[Any]				       =							os.path.join if is_local else posixpath.join

   UpperCAmelCase__      :					str				       =							"-TTTTT-SSSSS-of-NNNNN"
   UpperCAmelCase__      :					Union[str, Any]				       =							F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
   UpperCAmelCase__      :					List[Any]				       =							path_join(self._output_dir			,				snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							0
   UpperCAmelCase__      :					str				       =							0
   UpperCAmelCase__      :					Tuple				       =							0
   UpperCAmelCase__      :					Optional[Any]				       =							[]
   UpperCAmelCase__      :					Union[str, Any]				       =							[]

   for task_id, content in self._prepare_split_single(snake_case__			,				snake_case__			,				snake_case__ ):
     (
         (
         UpperCAmelCase__							
     )       ,			(
         UpperCAmelCase__							
     )       ,			(
         UpperCAmelCase__							
     )       ,			(
         UpperCAmelCase__							
     )       ,										
     )      :					Any				       =							content
     if num_bytes > 0:
       total_num_examples += num_examples
       total_num_bytes += num_bytes
       total_shards += num_shards
       task_id_and_num_shards.append((task_id, num_shards) )
       all_shard_lengths.extend(snake_case__ )

   UpperCAmelCase__      :					Tuple				       =							total_num_examples
   UpperCAmelCase__      :					int				       =							total_num_bytes

   # should rename everything at the end
   logger.debug(F"""Renaming {total_shards} shards.""" )
   if total_shards > 1:
     UpperCAmelCase__      :					Optional[Any]				       =							all_shard_lengths

     # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
     # pickling error due to pickling the SparkContext.
     UpperCAmelCase__      :					Any				       =							self._fs

     # use the -SSSSS-of-NNNNN pattern
     def _rename_shard(
         snake_case__		:  int			,				snake_case__		:  int			,				snake_case__		:  int			,				):
       rename(
           snake_case__			,				fpath.replace("SSSSS"			,				F"""{shard_id:05d}""" ).replace("TTTTT"			,				F"""{task_id:05d}""" )			,				fpath.replace("TTTTT-SSSSS"			,				F"""{global_shard_id:05d}""" ).replace("NNNNN"			,				F"""{total_shards:05d}""" )			,				)

     UpperCAmelCase__      :					Optional[Any]				       =							[]
     UpperCAmelCase__      :					Tuple				       =							0
     for i in range(len(snake_case__ ) ):
       UpperCAmelCase__       ,			UpperCAmelCase__      :					Optional[Any]				       =							task_id_and_num_shards[i]
       for shard_id in range(snake_case__ ):
         args.append([task_id, shard_id, global_shard_id] )
         global_shard_id += 1
     self._spark.sparkContext.parallelize(snake_case__			,				len(snake_case__ ) ).map(lambda snake_case__ : _rename_shard(*snake_case__ ) ).collect()
   else:
     # don't use any pattern
     UpperCAmelCase__      :					Dict				       =							0
     UpperCAmelCase__      :					Union[str, Any]				       =							task_id_and_num_shards[0][0]
     self._rename(
         fpath.replace("SSSSS"			,				F"""{shard_id:05d}""" ).replace("TTTTT"			,				F"""{task_id:05d}""" )			,				fpath.replace(snake_case__			,				"" )			,				)







 def 							UpperCamelCase  (					self		:  List[Any]			,				snake_case__		:  "datasets.SplitGenerator"			,				):



   '''simple docstring'''




   return SparkExamplesIterable(self.df )




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()